Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells390
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
Glucose is highly overall correlated with InsulinHigh correlation
Insulin is highly overall correlated with GlucoseHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
Insulin has 374 (48.7%) missing values Missing
BMI has 11 (1.4%) missing values Missing
Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 35 (4.6%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros

Reproduction

Analysis started2025-04-04 00:28:20.173426
Analysis finished2025-04-04 00:28:38.194149
Duration18.02 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:38.327876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2025-04-04T00:28:38.585320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Glucose
Real number (ℝ)

High correlation 

Distinct135
Distinct (%)17.7%
Missing5
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean121.68676
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:38.906972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.535641
Coefficient of variation (CV)0.25093642
Kurtosis-0.27703971
Mean121.68676
Median Absolute Deviation (MAD)20
Skewness0.53098853
Sum92847
Variance932.42538
MonotonicityNot monotonic
2025-04-04T00:28:39.260729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
111 14
 
1.8%
125 14
 
1.8%
129 14
 
1.8%
106 14
 
1.8%
102 13
 
1.7%
105 13
 
1.7%
112 13
 
1.7%
95 13
 
1.7%
Other values (125) 621
80.9%
ValueCountFrequency (%)
44 1
 
0.1%
56 1
 
0.1%
57 2
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
72 1
 
0.1%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Zeros 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.105469
Minimum0
Maximum122
Zeros35
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:39.607468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.7
Q162
median72
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.355807
Coefficient of variation (CV)0.28009082
Kurtosis5.1801566
Mean69.105469
Median Absolute Deviation (MAD)8
Skewness-1.843608
Sum53073
Variance374.64727
MonotonicityNot monotonic
2025-04-04T00:28:39.968627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
70 57
 
7.4%
74 52
 
6.8%
78 45
 
5.9%
68 45
 
5.9%
72 44
 
5.7%
64 43
 
5.6%
80 40
 
5.2%
76 39
 
5.1%
60 37
 
4.8%
0 35
 
4.6%
Other values (37) 331
43.1%
ValueCountFrequency (%)
0 35
4.6%
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
 
1.7%
52 11
 
1.4%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

Zeros 

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.536458
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:40.305189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.952218
Coefficient of variation (CV)0.77677549
Kurtosis-0.52007187
Mean20.536458
Median Absolute Deviation (MAD)12
Skewness0.1093725
Sum15772
Variance254.47325
MonotonicityNot monotonic
2025-04-04T00:28:40.652170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 227
29.6%
32 31
 
4.0%
30 27
 
3.5%
27 23
 
3.0%
23 22
 
2.9%
18 20
 
2.6%
33 20
 
2.6%
28 20
 
2.6%
31 19
 
2.5%
39 18
 
2.3%
Other values (41) 341
44.4%
ValueCountFrequency (%)
0 227
29.6%
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
11 6
 
0.8%
12 7
 
0.9%
13 11
 
1.4%
14 6
 
0.8%
15 14
 
1.8%
16 6
 
0.8%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%
48 4
0.5%

Insulin
Real number (ℝ)

High correlation  Missing 

Distinct185
Distinct (%)47.0%
Missing374
Missing (%)48.7%
Infinite0
Infinite (%)0.0%
Mean155.54822
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:40.978045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile41.65
Q176.25
median125
Q3190
95-th percentile395.5
Maximum846
Range832
Interquartile range (IQR)113.75

Descriptive statistics

Standard deviation118.77586
Coefficient of variation (CV)0.76359506
Kurtosis6.3705218
Mean155.54822
Median Absolute Deviation (MAD)55
Skewness2.1664638
Sum61286
Variance14107.704
MonotonicityNot monotonic
2025-04-04T00:28:41.317576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 11
 
1.4%
130 9
 
1.2%
140 9
 
1.2%
120 8
 
1.0%
94 7
 
0.9%
180 7
 
0.9%
100 7
 
0.9%
110 6
 
0.8%
115 6
 
0.8%
135 6
 
0.8%
Other values (175) 318
41.4%
(Missing) 374
48.7%
ValueCountFrequency (%)
14 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 2
0.3%
22 1
 
0.1%
23 2
0.3%
25 1
 
0.1%
29 1
 
0.1%
32 1
 
0.1%
36 3
0.4%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

Missing 

Distinct247
Distinct (%)32.6%
Missing11
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean32.457464
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:41.639497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.5
median32.3
Q336.6
95-th percentile44.5
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.9249883
Coefficient of variation (CV)0.21335581
Kurtosis0.86337903
Mean32.457464
Median Absolute Deviation (MAD)4.6
Skewness0.59396975
Sum24570.3
Variance47.955463
MonotonicityNot monotonic
2025-04-04T00:28:41.991867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.6 12
 
1.6%
31.2 12
 
1.6%
32.4 10
 
1.3%
33.3 10
 
1.3%
32.9 9
 
1.2%
30.1 9
 
1.2%
30.8 9
 
1.2%
32.8 9
 
1.2%
33.6 8
 
1.0%
Other values (237) 656
85.4%
(Missing) 11
 
1.4%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 3
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:42.314739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2025-04-04T00:28:42.665927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.268 5
 
0.7%
0.27 4
 
0.5%
0.263 4
 
0.5%
0.304 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2025-04-04T00:28:43.014761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2025-04-04T00:28:43.405462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2025-04-04T00:28:43.745660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-04T00:28:43.902306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2025-04-04T00:28:35.286773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:20.581307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:22.755427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:24.916031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:26.954255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:28.867078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:30.653398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:32.581805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:35.560657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:20.829270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:23.008588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:25.220281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:27.165414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:29.100161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:30.911105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:32.898472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:35.851108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:21.122438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:23.298956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:25.512949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:27.447883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:29.332939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:31.166757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:33.173708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:36.061867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:21.390138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:23.588360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:25.780632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:27.716992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:29.546685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:31.413417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:34.052403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:36.322894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:21.657608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:23.860503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:26.024787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:27.899358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:29.769038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:31.650467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:34.330564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:36.534798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:21.887653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:24.087210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:26.236624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:28.112627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:29.985191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:31.855368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:34.560632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:36.795794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:22.168919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:24.346233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:26.464985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:28.359504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:30.204510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:32.077572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:34.812119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:37.065333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:22.477307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:24.611606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:26.729764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:28.620701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:30.431244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:32.330398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-04T00:28:35.020406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-04T00:28:44.066854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1210.3510.0430.2830.2670.3140.607-0.067
BMI0.1211.0000.2820.1360.2280.3040.3170.0000.434
BloodPressure0.3510.2821.0000.0300.2350.1300.1520.1850.126
DiabetesPedigreeFunction0.0430.1360.0301.0000.0910.1300.173-0.0430.180
Glucose0.2830.2280.2350.0911.0000.6590.4810.1300.067
Insulin0.2670.3040.1300.1300.6591.0000.3550.1280.245
Outcome0.3140.3170.1520.1730.4810.3551.0000.2350.208
Pregnancies0.6070.0000.185-0.0430.1300.1280.2351.000-0.085
SkinThickness-0.0670.4340.1260.1800.0670.2450.208-0.0851.000

Missing values

2025-04-04T00:28:37.419509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-04T00:28:37.694160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-04T00:28:38.008657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.07235NaN33.60.627501
1185.06629NaN26.60.351310
28183.0640NaN23.30.672321
3189.0662394.028.10.167210
40137.04035168.043.12.288331
55116.0740NaN25.60.201300
6378.0503288.031.00.248261
710115.000NaN35.30.134290
82197.07045543.030.50.158531
98125.0960NaNNaN0.232541
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106.0760NaN37.50.197260
7596190.0920NaN35.50.278661
760288.0582616.028.40.766220
7619170.07431NaN44.00.403431
762989.0620NaN22.50.142330
76310101.07648180.032.90.171630
7642122.07027NaN36.80.340270
7655121.07223112.026.20.245300
7661126.0600NaN30.10.349471
767193.07031NaN30.40.315230